Unlike the mean squared error (MSE) that measures a pixel-to-pixel similarity, structural similarity (SSIM) [1]} measures the structure similarity between the two images. On the one hand, SSIM loss preserves the structure better than MSE loss. On the other hand, MSE preserves colors and luminance better than SSIM. To capture the best characteristics of both loss functions, we propose to combine them. Indeed, since precipitation maps contains many zero values, using unweighted loss function can erroneous the training. Thus, we added a weighting to the loss that reduces the influence of zero value pixels in precipitation maps.
